Large language models (LLMs) excel at synthesizing enormous amounts of information into personalized responses to plain-language prompts. These responses draw on massive training datasets and are often enhanced with internet searches. The fastest way to influence what LLMs say about your brand is to influence the content they retrieve through those searches.
At Evertune Research, we use the Evertune AI marketing platform to track hundreds of brands across 250 categories across every major LLM. This gives us clear insight into which content AI models cite most often, especially when users ask for brand or product recommendations across industries.
For this analysis, we reviewed the 6,000 most-cited URLs per model across ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overview, and Perplexity for March and April. We found that these models share a key behavior: they heavily cite listicles.
Half of LLMs’ most-cited URLs are listicles
Of the roughly 25,000 unique URLs we reviewed, half were listicles. Across nearly 400 million citations from all models, 63% pointed to listicles.
They’re tightly focused on a single topic, like “best laptops for gamers,” which makes them highly relevant to user prompts.
Their structured format also makes the content easy for models to parse and reproduce.
For brand-related queries, listicles do much of the work for LLMs by comparing products head-to-head on features, price, materials, and more—a format ChatGPT now features prominently in its shopping widget.
Listicles were pervasive across every model we reviewed. They accounted for 40–65% of the most-cited URLs, with Copilot at the low end and Gemini at the high end.
The vast majority of listicles in our analysis featured ranked lists, such as “Top 5 CRM Tools.” Depending on the model, these made up 71% to 86% of listicles. Unranked lists, such as “7 Ways to Save on Groceries,” were a distant second. Institutional rankings (e.g., data-heavy lists like U.S. News & World Report’s Best Colleges rankings) accounted for just 1.4% to 4.7% of listicles.
Corporate, earned media, and affiliate domains were the top sources of listicles in our analysis. It’s worth noting, however, that individual pages may contain affiliate content even when the broader domain does not.
For example, Forbes.com is an earned media domain, but it includes affiliate segments such as Forbes Advisor and Forbes Vetted. It ranked among the top three sources on every model for listicles in our URL dataset.
A word of warning before making listicles the foundation of a GEO strategy: Google has already signaled its intent to crack down on promotional listicles. Simply ranking your own brand No. 1 alongside competitors could also run afoul of a Federal Trade Commission rule that “prohibits a business from misrepresenting that a website or entity it controls provides independent reviews or opinions about a category of products or services that includes its own products or services,” among other prohibitions.
URLs that thrive on multiple models
We reviewed the 6,000 most-cited URLs across six LLMs, which in theory produced a pool of 36,000 URLs. In practice, the dataset contained about 25,000 unique URLs, since many appeared among the most-cited results across multiple models.
Among the models, the three Google Gemini-powered models — Gemini, AI Mode, and AI Overviews — showed the highest overlap. More than half of Google AI Mode’s most-cited URLs also appeared among Google AI Overviews’ most-cited URLs. Gemini likewise shared a large portion of its top-cited URLs with both Google AI Mode and Google AI Overview.
The remaining models also shared the most URLs with Google AI Mode and Google AI Overviews, though the overlap was much smaller. Perplexity shared more than 20% of its URLs with both models, while ChatGPT shared more than 15% with each.
Given the thousands of URLs models cite on any topic, that still represents meaningful overlap. Copilot, by contrast, shared just 4% to 6% of its URLs with any other model.
The URLs that models cite most deviate for many reasons, including model training, sites’ crawl permissions and other factors. Traditional SEO that moves content higher in search results, no matter if the search is by a bot or a person, also plays a role, especially for Google AI Mode and Google AI Overview.
Page components of heavily cited URLs
Our review of the roughly 25,000 URLs heavily cited by LLMs found that these pages typically ranged from 1,000 to 2,000 words, averaged 18 words per sentence, linked frequently, and used structured headings (H2s and H3s) throughout.
Copilot favored the most concise content, typically citing pages with 964 words and 24 paragraphs. Gemini skewed more verbose, typically citing pages with 1,977 words and 53 paragraphs.
Although there’s no cookie-cutter formula for success in AI visibility, we found that the most-cited pages typically included the following components:
GEO takeaways
Each LLM has its own preferences and quirks, and a strong GEO strategy accounts for them. But our analysis of more than 25,000 URLs suggests that some GEO best practices can improve brand visibility and sentiment across models.
All LLMs cite large volumes of highly structured, hyper-specific content, which listicles exemplify. Avoid spammy, self-promotional listicles that Google penalizes, but otherwise aim to create and appear in lists where relevant.
Traditional SEO supports GEO. Pages that perform well in human search results also tend to perform well in bot-driven searches. This is especially true for Gemini-based models.
Pay attention to the page structures most often cited by the model you want to target. Copilot tends to favor brevity, while Gemini responds better to more expansive content. In general, keep pages under 2,000 words, use frequent links, apply strong structure, and include images and lists when relevant.
As part of the WTS Global Week celebrations, join Yoast and Women in Tech SEO for a special online coffee chat celebrating two incredible community milestones: 7 years of WTS and 16 years of Yoast.
SEO has always been more than algorithms, rankings, and updates; it’s built through people sharing ideas, supporting one another, and learning together. In this relaxed and inspiring session, Carolyn Shelby, Samah Nasr, and Areej AbuAli will reflect on the power of community in shaping careers, building confidence, and helping the SEO industry grow into a more collaborative and inclusive space.
Have you ever wondered where SEO professionals really learn beyond courses and documentation? Or how people find mentors, supportive communities, and opportunities to grow in the industry? Maybe you’re just starting out and trying to figure out which resources are actually worth your time.
Together, we’ll talk about how community creates learning opportunities, opens doors for newcomers, and provides the support people need to grow in SEO. Expect practical tips, career insights, honest experiences, and advice for those looking to deepen their involvement in the industry and connect with others in the space.
The session will include a 30-minute community chat followed by a live Q&A with attendees, giving everyone the chance to join the conversation and share their perspectives.
Bring your coffee or tea, questions, and stories; we’d love for you to be part of it.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-19 10:22:012026-05-19 10:22:01Yoast x WTS Global: SEO is built in community
You prompt ChatGPT with something, and suddenly your brand name shows up in the response. Sounds like a win, right? But before you share the screenshot with your team, there’s one important question to ask: Is your brand being cited or mentioned?
As AI search and LLM-driven discovery continue to grow, understanding the difference between AI brand mentions and AI citations is becoming increasingly important for SEO and brand visibility. In this article, we’ll break down what AI brand mentions are, how they work, and how they differ from citations.
Since we know you’re excited to celebrate your AI visibility win, let’s get straight into it.
AI brand mentions occur when an AI tool references your brand in responses, while citations support the information with sources
Understanding the difference between mentions and citations is crucial for SEO and brand visibility
To improve AI mentions, create clear, structured, and extractable content that addresses user queries directly
Brands need to build authority through trusted mentions across various platforms to enhance visibility and acceptance by AI systems
Both mentions and citations are crucial; mentions help AI identify your relevance, while citations reinforce your credibility
What is an AI brand mention?
An AI brand mention happens when an AI tool references your brand name inside a generated response, recommendation, comparison, or summary. The brand mentions can be either linked (also known as explicit mention) or unlinked (also known as implicit mention).
Here’s an example of ChatGPT’s response to, “What are some of the best WordPress SEO plugins?”
ChatGPT mentions Yoast SEO explicitly and implicitly
AI can mention brands in different conversational contexts depending on the user’s query and intent. Here are some of the most common ways AI-generated responses include brand mentions:
Direct recommendations
This happens when AI directly suggests a brand, product, or service as a possible solution to the user’s query. For instance, these mentions typically appear in recommendation-style prompts where users are actively seeking options or tools.
Comparisons
AI may mention brands while comparing products, services, features, pricing, or use cases. In such cases, the brand becomes part of a broader evaluation or decision-making discussion.
Examples within answers
Sometimes, AI uses brands as examples to explain concepts, trends, workflows, or industry practices. These mentions help provide context and make the explanation easier for users to understand.
Contextual references
Brands can also naturally appear in broader discussions about a topic or industry. These mentions are less promotional and more about establishing topical relevance within the conversation.
How do LLMs decide what to mention?
Large language models don’t “choose” brands the way a human would. They generate responses based on patterns, probabilities, and signals they’ve learned over time. When a brand shows up in an AI answer, it’s usually because multiple underlying factors align.
LLMs learn from vast datasets that show how often certain brands appear alongside specific topics.
When people repeatedly discuss a brand in connection with a particular use case, the model develops a strong association. Over time, this increases the likelihood that the brand will appear in responses to similar queries.
But it’s not just frequency. Context matters just as much.
What topics is the brand linked to?
What problems does it appear to solve?
What other terms show up around it?
Brands that appear across multiple contexts build deeper, more flexible associations. Those with limited or inconsistent mentions struggle to surface.
2. Retrieval-Augmented Generation (RAG)
Many modern AI systems extend beyond their training data using Retrieval-Augmented Generation (RAG). This is where things get more dynamic, and where many brands either gain visibility or disappear entirely.
At a basic level, here’s what changes:
Without RAG, the model answers using only what it learned during training
With RAG, the system first retrieves relevant information from external or live sources, then passes both the user query and the retrieved content into the model
The model then combines this new information with its existing knowledge to generate a more accurate, up-to-date response.
Descriptive diagram of RAG and training data by Amazon AWS
When a user submits a query, the retrieval system acts as a gatekeeper. It scans indexed sources, such as web pages, documentation, articles, and forums, to find content that best matches the query.
3. Context and semantic understanding
LLMs don’t rely on exact keyword matches. They interpret intent. When someone asks a question, the model maps it to broader concepts and then surfaces brands that fit those meanings.
For example, a query about “tools for remote teams” might connect to:
Collaboration
Async work
Team communication
Workflow management
LLMs are more likely to surface brands that consistently associate themselves with these ideas, even if users don’t use the exact phrase. This is where entity clarity becomes critical. If your brand is described differently across sources, the model struggles to understand what you actually do.
Overall, it’s not just about what you say, but how your content connects to related topics. Therefore, linking your brand to relevant concepts, use cases, and terminology helps AI systems understand when your brand is relevant. This is where it helps to semantically link entities to your content, so those relationships are clearer and easier for models to pick up.
4. Authority and cross-source validation
LLMs don’t rely on a single source. They validate information by comparing patterns across multiple sources and weighing the trustworthiness of those sources. When a claim appears consistently across many independent platforms, the model is more confident in including it. If it shows up in only a few places, that confidence drops.
AI systems combine semantic understanding with retrieval signals to assess which sources to trust. This typically includes:
Source credibility: Well-known publications, academic content, government sites, and recognized organizations are prioritized
Citation patterns: Sources that are frequently referenced by others are treated as more authoritative
Recency: More recent information is often weighted higher, especially for fast-changing topics
Transparency: Content with clear authorship, dates, and references is considered more reliable
Authority in AI is about being consistently referenced across credible, independent sources. This is why PR, earned media, and third-party mentions play a bigger role in AI visibility than they traditionally did in SEO.
5. Relevance to the query
Before anything else, the model evaluates fit. Even highly authoritative or frequently mentioned brands won’t appear unless they clearly match the user’s intent, such as the use case, audience, or problem being solved.
In simple terms, if your brand isn’t a strong answer to the query, it won’t be included.
When surfacing a brand in answers, AI models may include nuances like:
Beginner vs advanced users
Budget vs premium solutions
Niche vs general use cases
Modern AI systems have shifted from traditional keyword matching to query understanding. They use Natural Language Processing (NLP) to understand the “why” behind the text strings. If explained technically, gen AI converts text queries (prompts) into vectors that allow it to find semantic similarity and return relevant answers.
6. Sentiment and human feedback (RLHF)
LLMs don’t rely solely on training data or web sources. They are continuously improved through human feedback, a process known as Reinforcement Learning from Human Feedback (RLHF).
In this process, human evaluators review model responses and guide them based on whether the answers are:
Helpful
Accurate
Safe
Trustworthy
How does this affect brand mentions? If a brand is consistently associated with negative sentiment, the model may learn to avoid or deprioritize it. On the other hand, brands that appear in neutral or positive contexts across sources are more likely to be included.
In this way, RLHF acts as a layer that refines raw data signals, aligning brand mentions more closely with quality, trust, and user expectations.
Tips to get more mentions
Getting your brand mentioned in AI answers isn’t a completely new discipline. It closely overlaps with what many now call LLM SEO. If you’ve already been working on visibility, authority, and content quality, you’re on the right track.
Here are a few practical ways to improve your chances of being mentioned:
Publish definitive, extractable resources
Create content that is easy for AI systems to understand and reuse. This means clear definitions, structured explanations, and direct answers rather than long, vague introductions.
For example, a well-structured guide that clearly defines “what is customer data management” with concise sections is far more likely to be picked up than a generic blog post that buries the answer halfway through.
Address evaluative queries
AI assistants often respond to questions like “best tools for X” or “which platform should I choose?” If your content directly addresses these comparisons, you increase your chances of being included.
Like a comparison page, for example, Yoast vs. Rank Math, that explains when your product is better suited than alternatives, it gives the model a clear context to recommend you.
Strengthen authority signals
Mentions across trusted, independent sources significantly improve your visibility. This includes being featured in industry publications, contributing expert insights, or earning mentions in reviews and comparisons.
For example, a brand cited in multiple reputable blogs and reports is more likely to be surfaced than one that only publishes content on its own website.
Keep cornerstone pages current
Freshness plays a key role, especially for topics that evolve quickly. Regularly updating the content of your key pages signals that your information is reliable and up to date. For example, a “best tools” page updated every few months with current data is more likely to be retrieved than one that hasn’t been touched in years.
Broaden entity clarity
Your brand should be consistently described across your website and external platforms. This helps AI systems clearly understand what you do and when to mention you. For example, if your product is always positioned as “project management software for remote teams,” that repeated clarity strengthens your association with that use case.
AI brand mentions vs AI citations
Before sharing the comparison, let me give you a brief overview of citations. AI citations are references that AI systems and search engines include to support the answers they generate.
Citations usually point to a specific source, such as a webpage, report, or article, and credit the source of the information. In many cases, a response can include both a brand mention and a citation at the same time.
ChatGPT’s response mentions brands and cites resources to back its answer
Next, let’s see how they are different.
Aspect
AI brand mention
AI citation
Definition
Your brand name appears within the AI-generated response
AI attributes information to your content, often with a link or reference
Format
Mentioned naturally in text, no link required
URL, footnote, or inline source reference
What it signals
Brand awareness and category relevance
Authority, credibility, and trustworthiness
Impact
Builds mindshare and keeps you in the consideration set
Acts as proof of expertise and can drive traffic
Traffic potential
Indirect, through increased brand recall
Direct, via clickable or attributed sources
Frequency
More common across most AI responses
Less common and more competitive
Where it appears
Across most LLMs, even without live web access
More common in systems with retrieval or web access
How to optimize
PR, earned media, third-party mentions, community presence
Create citation-worthy content, structured data, original research
Mentions get you in the conversation. Citations make you the source.
Mentions make the AI familiar with your brand. Citations make the AI willing to vouch for it.
In short, the most effective strategy is to optimize for both.
Do citations still matter?
Yes, citations still matter, but they are no longer a standalone strategy.
AI systems still use citations as supporting signals to validate information, confirm credibility, and discover trustworthy sources. When multiple reputable websites reference the same brand or source, it reinforces trust and helps AI systems verify the information’s reliability.
While both mentions and citations matter, mentions currently carry more weight for relevance and AI visibility. Citations still help reinforce authority and trust, but mentions give AI systems richer contextual signals about where a brand fits, how often it appears in conversations, and why it matters within a topic.
How to achieve citations and mentions both?
Brands that consistently appear in relevant conversations while publishing credible content are more likely to earn both mentions and citations. Here are some easy strategies that you can follow:
Create mention-worthy content
The easiest way to earn both mentions and citations is to publish content people naturally want to reference. This includes thought leadership, original research, unique insights, industry commentary, and practical resources that add real value. When your content contributes something new to the conversation, it becomes easier for journalists, creators, communities, and AI systems to pick it up.
Focus on contextual brand mentions
AI systems pay attention to how and where your brand is discussed. Mentions across community discussions, industry blogs, PR coverage, podcasts, forums, and trend-based conversations help reinforce your relevance within a topic. The goal is not just visibility, but also appearing consistently in meaningful, context-rich discussions.
Build credibility for citations
If you want more citations, credibility becomes essential. AI systems are more likely to reference content that demonstrates strong expertise and trustworthiness. This is where principles like E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) become important.
AI brand mentions vs. citations: FAQs
While mentions help AI systems recognize and associate your brand with specific topics, citations strengthen trust and authority by validating your content as a reliable source.
The reality is that both work together. Brands that consistently appear in relevant conversations while publishing credible, high-quality content are far more likely to strengthen their AI visibility over time.
Here are some common questions around AI brand mentions and citations:
Are citations and backlinks the same?
Not exactly. Backlinks are traditional SEO links that point from one website to another, mainly to help search engines understand authority and ranking signals. AI citations, on the other hand, are references AI systems use to support or validate the answers they generate. While citations can include links, their primary role is attribution and trust rather than passing ranking value. For a deeper understanding, read AI citations vs backlinks.
If a brand is mentioned, will it be cited too?
Not always. A brand can be mentioned in an AI response without being directly cited as a source. This usually happens because AI systems often recognize brands through repeated contextual mentions across the web, even when they are not using that brand’s content as the primary supporting source for the answer.
Why should businesses focus on both mentions and citations from AI?
Mentions and citations support different aspects of AI visibility. Mentions help AI systems understand where your brand fits within a topic, while citations reinforce authority and trust.
How to track both mentions and citations for my brand?
Tracking AI visibility manually across platforms can quickly become difficult. Tools like Yoast SEO AI+ help brands monitor how they appear across AI-driven search experiences. With AI Brand Insights, you can track mentions, citations, and overall brand presence across AI platforms to better understand where your visibility is growing and where opportunities exist to improve your AI brand visibility using Yoast AI Brand Insights.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-19 06:48:132026-05-19 06:48:13What are AI brand mentions? And how are they different from citations?
An AI visibility report tracks how often your brand is cited across AI-generated responses. Think of it as a companion to your SEO reporting, not a replacement for it.
Your tracked prompt set is the foundation of every number Writesonic shows you. If you don’t understand what those prompts cover, you’ll misread your data.
Portfolios organize your tracked URLs by content type. Get this set up early and keep it updated as new content goes live.
Citation data is inherently noisy. A single-period dip rarely means anything. A sustained two-to-three-month trend does.
The Action Center is where the quick wins live. Use it to find pages with citation visibility gaps and start closing them.
Here’s something that should keep marketers up at night: your buyers are researching purchases in ChatGPT and Perplexity, and most brands have no idea whether they’re showing up in those answers.
That gap is exactly what an AI visibility report is built to close. It tells you how often your brand gets cited in AI-generated responses, which pages are driving those citations, and where competitors are outperforming you in the moments that matter most.
Writesonic has one of the more practical toolsets for building this kind of reporting. But I want to make one thing clear: I’m not trying to do a review of the platform. This is a working guide for content teams that need to get this reporting off the ground and want to understand what the data actually means before they put it in front of a client or a leadership team.
Why AI Visibility Reporting Matters for Marketing Teams
Buyers don’t just Google things anymore. A growing portion of them open ChatGPT, type a question, and act on whatever comes back. Salesforce research found that 41 percent of consumers used AI tools as part of their research process in 2024. That number has only grown since.
If your brand isn’t being cited in those responses, you’re losing potential customers.
AI visibility reporting helps you understand not just if you appear, but which topics you’re being cited for, how that’s changing over time, and who’s beating you in the answers your buyers are reading.
Where this fits in your stack matters, too. AI visibility reporting isn’t a replacement for organic search analytics or conversion data, but an added signal. This tells you whether AI systems find your content credible enough to surface. Teams that treat it as a complement to their larger organic strategy get more out of it than those trying to use it standalone.
The two questions it should help you answer: Are we showing up where buyers are actually looking? And if not, what do we fix first?
Understanding Your Prompt Set Before You Report on Anything
Every number in Writesonic traces back to your tracked prompt set. These are the specific questions the platform monitors across ChatGPT, Perplexity, Gemini, and other AI tools to see whether your content gets cited in the response.
Get this wrong, and everything downstream looks worse than it is.
The platform assigns default topic labels to clusters of prompts. Those labels are usually broad. A marketing blog running this kind of reporting might see their prompt topics labeled “content marketing” and “digital marketing.” Both are accurate but they are closely related terms that cover a huge swathe of subtopics. Due to the lack of specificity, you may encounter issues building and reporting on AI visibility if you only rely on the pre-populated topic list.
Here’s what works better: export the full prompt list, drop it into an AI tool, and ask it to summarize the underlying themes, intent types, and audience categories. That same marketing agency’s list of 100 prompts might actually break into much more specific themes, like Organic & search visibility, Paid media & SEM, and Email & conversion.
The screenshot above is a portion of Claude’s output when I asked it to perform this exercise. As you can see, there’s a lot more information here to guide our content reporting (and creation). Not only do we have a clearer idea of the GEO content pillars we’re tracking against, but also the audience and intent for each category.
This type of output influences how you read everything else. If you find that your prompt set skews heavily toward one audience, your citation numbers for content aimed at a different audience will look artificially low. You can’t treat this as losing ground. You’re just being measured against prompts that page was never written for.
The practical rule: only report on content that genuinely aligns with your tracked prompt themes. Flagging low citation share on a page that serves a completely different audience creates confusion in client reports. Know your prompt set first, then interpret your data.
To pull the list, navigate to the Prompts section and use the export option. Fifteen minutes of AI-assisted theme analysis is worth doing before you touch anything else.
Setting Up Portfolios to Track Your Content Over Time
Portfolios are folders. They allow you to organize the URLs you’re tracking by content type so you can report on categories rather than hunting down individual pages every time you pull a report.
Create them early and keep them simple. At minimum, you want separate portfolios for blog posts, core website pages, and comprehensive guides. If your client has distinct product lines or service areas, break those out too.
The part that really matters is the workflow. As soon as a new piece of content goes live, add the URL to its portfolio. Teams that skip this step spend far too much time during reporting cycles searching for pages that should have been tracked from day one. Make it part of the implementation process: publish, review, then add to portfolio.
One thing worth knowing: portfolios aren’t limited to your own content. You can add competitor URLs and track their citation performance in the same view. That’s useful when you need to show a client exactly where a competitor is outpacing them on a specific topic, without having to cross-reference separate reports mid-meeting.
How to Report on a Single Piece of Content
The path is: Overview > Citations > Content Performance. Set your date range and filter by URL slug.
You’ll mainly want to look at Citation Count or Citing Answers, which are how many times that page was cited across all tracked prompts in the selected period.
If you look at Citation Share, the number may appear small. That’s because this view measures a single page’s citation contribution across your entire prompt set, not just the prompts that are relevant to what the page covers. A tightly focused blog post will naturally have limited citation surface area relative to the full prompt universe you’re tracking.
Second, pay attention to the prompts the page is and more importantly, is not being cited for. You can see the full prompt set by clicking on the number in the ‘Answers citing your content’ tab. In this case, I clicked on the 100.
You’ll then be taken to the All Prompts & Answers view, where you can see which prompts and platforms are surfacing your content and which ones are not.
If a page is ranking well for some prompts but missing others that closely match its content, those gaps are actionable. Adding a structured FAQ section or a more direct answer to a specific question can sometimes close them — and that’s something Writesonic can help you generate.
Third, be careful with month-over-month comparisons. A single dip is not a signal. LLM citation patterns shift constantly as models update and competitive content changes. Before treating a decrease as a problem, remove the comparison period and look at a three-to-four-month trend line instead. A trough followed by recovery is a very different story than a genuine sustained decline.
When you do see a real downward trend, don’t touch the content first. Cross-reference with your SEO data and generative engine optimization metrics. Often, the issue is external, like a model update, and editing the content won’t fix it.
Reporting Content Categories with Portfolios
Another useful feature inside Writesonic is the ability to report on content performance at the portfolio level, not just the page level.
To access it, navigate to Overview > Page Tracker > Portfolios. If you’ve organized portfolios by content type, topic cluster, service area, or funnel stage, this view gives you a meaningful way to evaluate how a group of pages is collectively performing in AI-generated answers.
This matters because page-level reporting only tells you so much. When you’re managing a content program at scale, you need to be able to say, “our informational content about hotel amenities is being cited regularly” or “our location-based pages are getting picked up but not driving brand mentions.” Portfolios let you have that conversation at the category level, which is how most content strategies are built and how most stakeholders think about performance.
Two metrics worth understanding here are citation share and visibility contribution.
Citation share tells you what percentage of all AI answers cite at least one page from that portfolio. Think of it as reach for that content category. A 1.6% citation share, like the example above, means those pages appeared in roughly 660 out of 40,000 tracked answers. Reported at the portfolio level, this becomes a concrete benchmark you can share: how often AI tools are drawing from this type of content, and how that’s trending over time.
Visibility contribution is a layer deeper. It measures the percentage of your brand’s total AI visibility that comes from pages in that portfolio being cited alongside a brand mention. It tells you which content categories are driving brand recognition in AI answers, not just traffic or citations. A portfolio with strong visibility contribution means your content and your brand name are appearing together in AI responses, which is the outcome you’re optimizing for.
Together, these two metrics help you go beyond vanity reporting and start answering the questions clients and stakeholders actually care about: Is this content working? Are people seeing our brand name? Which content categories should we double down on, and which need attention?
If a portfolio has solid citation share but low visibility contribution, AI tools are referencing those pages frequently but not associating them with your brand. That’s a signal to look at how clearly your brand is represented within the content itself. If a portfolio is underperforming on both, that’s a prioritization conversation. And if a portfolio is driving strong numbers on both, that’s proof-of-concept worth scaling.
Understanding Volatility: What’s Signal and What’s Noise?
LLM citation data is noisy by nature. This isn’t a Writesonic-specific problem. It’s how these models work. AI citation drift, where sources shift in and out of responses as models retrain, re-rank sources, or adjust sampling, has been documented across platforms. Research from SISTRIX shows citation sources can change significantly week over week, even when the underlying content is untouched.
One data point tells you almost nothing. The question is always whether you’re looking at a trend or a snapshot.
For example, look at the graph above. This shows the number of citations a page has over a two-month span. As you can see, there are several peaks and valleys, even within the span of a few days. However, if you were to draw a trend line, the result would be relatively flat and even increase a bit towards the end of the second month.
That’s why it’s important to remember that a one-period decrease is not a call to action. A consistent downward pattern over two to three months is worth digging into. Before you touch any content, pull SEO performance and AI Overview impression data for the same window. If organic traffic is stable and AI Overview appearances are flat, the Writesonic dip is most likely a model or sampling artifact.
This is worth saying explicitly to leadership and clients. AI visibility reporting is newer and messier than traditional SEO reporting. Setting that expectation upfront builds credibility. Trying to explain unexpected volatility after the fact does the opposite.
What Writesonic Can’t Tell You
Transparency on limitations makes reporting more credible, not less.
As mentioned earlier, Writesonic tracks a defined prompt set, not every AI query relevant to your category. Your citation numbers reflect performance within that sample. That distinction matters when someone asks why results look lower than expected. The tracked set may simply not cover the full range of queries where your content performs well.
Other things to be aware of include:
Prompt volume isn’t search volume. AI platforms don’t publish query data the way Google does. Estimating how many times people search specific prompts in platforms like ChatGPT requires multiple data sources, a scoring methodology, and sampled user data. That means LLM prompt volume should always be taken with a grain of salt, no matter what AI visibility platform you’re using.
Citation change versus buyer behavior. A drop in citations might reflect a model update or a competitor adding a stronger page. It doesn’t necessarily mean fewer buyers are encountering your brand. Separating those two things requires additional data sources like conversion tracking, qualitative research, or broader competitive analysis.
Competitive visibility outside the tracked set. You can see how competitors are performing within your prompt set. You can’t see how they’re performing in AI queries you aren’t tracking at all.
For each gap, the fix is the same: layer in additional signals. Use organic performance, GEO and AEO analysis alongside broader competitive research to paint the full picture. Writesonic works best as one input among several, not as a standalone source of truth.
Using Quick Wins to Improve AI Visibility Now
The Action Center is where the most immediately actionable reporting lives. Navigate to Action Center > Boost Content Visibility > Refresh existing content for AI visibility to find existing pages where competitors are being cited more often than you for the same prompts.
These are your quick wins. The pages themselves usually aren’t the problem; they’re just missing specific structural elements that AI models tend to pull from. Common recommendations from the platform include FAQ sections, comparison tables, and explicit key takeaway sections. These signal to large language models (LLMs) that a page directly answers a specific question and improves your chances of being cited.
Writesonic will generate draft versions of those elements for you. Use them as a starting point, not a final output. Editorial judgment still applies. Not every recommendation fits every page. A conversion-focused product page probably shouldn’t get a sprawling FAQ section that complicates the user journey, even if the data suggests it would improve citation share.
This module is particularly useful at campaign kick-off. Teams can surface concrete page improvements in the first few weeks while the broader strategy is still being developed, giving clients something tangible early.
New Content Opportunities in the Action Center
Beyond refreshing existing pages, the Action Center also identifies topics where competitors are earning citations, and you have no content covering them at all.
Navigate to Action Center > Boost Content Visibility > Create content inspired by competitors winning in AI citations for this view. The recommendations here are about where to create new pages or blog posts, not about tweaking what you have. If a competitor is consistently cited on a topic that aligns with your tracked prompt themes and your site has nothing on it, that’s a real gap in your AI visibility coverage, and a direct input for your content calendar.
Review this section at least quarterly alongside your standard keyword research. The two often point in the same direction.
FAQs
What KPIs matter for executive AI visibility reporting?
Lead with citation share trend direction over a rolling 90-day period, not raw citation counts. Raw numbers require too much context without supporting data. Showing category-level performance for priority topics, plus specific wins and gaps, lands better in executive reporting than a single number that needs a two-paragraph explanation.
How do you create reports showing brand visibility in AI platforms?
Use Writesonic’s Content Performance and Page Tracker views to pull citation data by URL and topic. Present directional trends and be explicit about what your prompt set covers.
How do you report AI search visibility to leadership?
Frame AI visibility as one signal alongside organic search, not a standalone metric. Show specific wins (pages gaining citation share) alongside gaps, and tie recommendations directly to business priorities. Explain volatility upfront so a single-period dip doesn’t derail an entire reporting session.
Where can you find AI visibility reports with sentiment analysis?
Writesonic includes sentiment indicators alongside citation data. You can dig deeper into how your brand is being discussed on LLMs by navigating to Overview, then the Sentiment dashboard under Brand Visibility.
Conclusion
Most teams that struggle with AI visibility reporting don’t have a data problem. They have an interpretation problem. The numbers look strange, the volatility is hard to explain, and it’s difficult to know what to act on.
Writesonic helps with that, but only if you come in with the right expectations. Know what your prompt set covers. Organize your portfolios from the start. Read citation data as a directional trend, not a precise scorecard. Use the Action Center to find the generative engine optimization improvements most likely to move the needle quickly. Teams that build these habits now will be ahead of the curve as AI-driven search grows and the tools mature.
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As people increasingly gravitate to generative AI experiences and find information in new ways,
we’re publishing a new resource to help website owners, SEOs, and developers understand how to
optimize their content for appearance in generative AI features in Search, and in turn Google
Search overall.
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https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/05/seo-guide-illustration-CZnE1C.png?fit=960%2C517&ssl=1517960Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-14 15:43:152026-05-14 15:43:15The Complete SEO Guide for Beginners
Keyword volume is one signal, not the full story. It tells you that demand exists, but not where it lives, how it’s being answered, or whether your brand is part of the conversation.
The Ubersuggest keyword tool and Answer the Public now pull data from Google, Bing, YouTube, TikTok, Instagram, and Amazon, giving you a multi-platform view of where your audience is actually searching.
AI tools like ChatGPT and Gemini generate answers, not link lists. Ubersuggest’s AI Search Visibility feature tracks whether your brand appears in those answers and how your visibility compares to competitors.
Ubersuggest’s global keyword data lets you identify regions where demand already exists for your product or service, so you can prioritize expansion instead of guessing.
The highest-value content opportunities sit at the intersection of strong multi-platform demand and low brand visibility. Knowing where that gap is tells you exactly where to focus.
Search is no longer a single-channel game. For a long time, SEO meant one thing: get found on Google. But Google’s own SVP Prabhakar Raghavan noted that roughly 40 percent of young people now turn to TikTok and Instagram for searches instead of Google, a number that’s only likely to grow over time.
Add ChatGPT, Gemini, YouTube, and other rising channels on top of that, and the picture becomes clear: keyword volume alone can’t tell you where demand actually lives, how it’s being answered, or whether your brand is part of the conversation.
The good news is that Ubersuggest is a great tool to help you adapt to this shift. I’ll cover here how Ubersuggest keyword ideas data actually surfaces, and how to layer multiple signals into a strategy built for the way search works today.
What Keyword Data Actually Tells You (And What It Doesn’t)
Keyword research is still the foundation of any solid content strategy. Search volume tells you how much interest exists around a topic. Keyword difficulty helps you gauge how competitive that space is. Search intent tells you what kind of content actually fits the query. All of that is genuinely useful, and none of it is going away.
But traditional keyword data was built for a world where Google was the only game in town. That world doesn’t exist anymore.
A user today might search “best email marketing tool” on Google, watch comparison videos on YouTube, follow threads on Reddit, scroll TikTok for creator recommendations, and then ask ChatGPT for a final opinion before choosing a product. Each of those touchpoints is a moment of demand. Most keyword research tools only capture one of them.
The practical result: you can have a well-optimized piece ranking on page one for a target keyword and still be invisible to a significant chunk of your audience. That’s not a traffic problem you can fix by adjusting your meta tags.
Two questions worth asking before you build any content plan:
Where does demand for this topic actually live across platforms?
Is my brand showing up when people ask AI tools about this subject?
Ubersuggest addresses both. Here’s how each capability works.
How the Ubersuggest Keyword Tool and Answer the Public Surface Multi-Platform Demand
If you used Answer the Public a few years ago, it was a visualization tool that pulled suggestions from Google Autocomplete. Useful, but limited to one platform.
That’s no longer what it is. Answer the Public (now integrated with the Ubersuggest keyword generator) pulls keyword and hashtag data from Google, Bing, Amazon, YouTube, TikTok, and Instagram. That’s a meaningful shift. You’re not just seeing what people type into a search bar anymore. You’re seeing what they watch, hashtag, and shop for across the platforms where they actually spend their time.
Here’s what that looks like in practice. Enter a broad keyword like “marketing” and select a platform.
Switch to Instagram and you’ll see the hashtags your audience is actively using around that topic. Switch to TikTok and you get a keyword wheel showing what creators and users are searching within the app.
You can also compare how results shift over time, which tells you whether interest in a topic is growing or fading on a specific platform. That matters for content planning. A keyword might have modest Google search volume but strong TikTok traction, which is a signal that short-form video would outperform a blog post for that topic. You’d never see that from Google data alone.
For content teams, this changes the planning conversation. Rather than asking “what should we write?” you start asking “what format and platform does this topic actually call for?” That’s a more useful question, and it leads to content that actually reaches people where they’re searching. For a closer look at using the two tools together, see how to use Answer the Public with Ubersuggest.
The AI Search Layer: What Ubersuggest’s AI Visibility Data Shows You
Multi-platform keyword data covers where demand lives across traditional and social search. AI Search Visibility covers something different: whether your brand shows up when AI tools answer questions in your category.
The distinction matters more than it might seem. When someone asks ChatGPT “what’s the best CRM for a small sales team?” they don’t get ten blue links to evaluate. They get a generated answer. Your brand is either mentioned in that answer or it isn’t. There’s no page-two for AI responses.
This is the core challenge of AI search: it’s not about ranking, it’s about being cited. And right now, most brands have no systematic way to know whether they’re being cited at all.
Ubersuggest’s AI Search Visibility feature is built to solve that. It runs repeated queries across AI platforms, aggregates the results, and gives you a clear, data-backed picture of how often your brand appears in AI-generated responses for your most important topics. One AI response is a data point. Hundreds of responses is a pattern.
The feature surfaces four key metrics:
Brand Visibility %: How often your brand is mentioned across aggregated AI responses for relevant prompts.
Industry Rank: Where you sit relative to competitors in your space.
Top Prompts table: The specific questions and prompts where your brand does and doesn’t appear in AI answers.
Competitor Visibility trend chart: How competitors’ AI presence is changing over time.
A note on variability: AI responses are inherently inconsistent. Ask the same question twice and you may get a different answer, different brand mentions, or a different level of detail. That’s normal, and it’s exactly why aggregating data across hundreds of repeated queries gives a more reliable read than spot-checking a single response on a given day.
One of the most actionable outputs from this feature is the Top Prompts table. It tells you which specific AI search prompts are driving brand visibility in your category, and which prompts your competitors are dominating without you. Those gaps are your content brief.
Ubersuggest’s AI visibility features are built to cut through that noise, aggregating responses at scale so your visibility score reflects a real pattern rather than a single snapshot. This is the piece of Ubersuggest keyword research that most marketers haven’t built into their workflow yet. The window to get ahead of competitors here is still open, but it won’t be for long.
Going Global: Using Ubersuggest Data Across Markets
Expanding into new markets is one of the highest-leverage growth moves a brand can make, and one of the most expensive to get wrong. NP Digital now operates in 19 countries, and that growth wasn’t built on guesswork. It came from identifying where demand already existed and going after the regions with the clearest signal first.
Ubersuggest’s global keyword data makes that analysis accessible without a research team. Type any keyword into the Ubersuggest keyword tool, run a search, and filter by country. You’ll see where search volume for your topic is concentrated across global markets.
The insight here is about prioritization. You don’t need to tackle every market at once. You need to find the markets where demand already exists for what you offer, because those are the ones where content and campaigns can work with the grain of existing intent rather than trying to create it from scratch.
Layer in the city-level targeting from AI Search Visibility and you get a second useful data point: not just where people are searching, but where your brand is (or isn’t) showing up in localized AI responses. A market might have strong keyword volume and competitors with high AI visibility, or it might have strong volume and very little AI presence from anyone, which is a wide-open opportunity. That combination turns global expansion strategy from a gut call into a data-backed decision.
For most brands, the low-hanging fruit is closer than it looks. Start by running your core keywords through the global filter and see which regions surface demand you’re currently not serving.
How to Put It All Together
The data points covered above aren’t meant to live in separate tabs. Here’s how to run them as a single workflow.
Step one: map where demand lives.
Use the Ubersuggest keyword tool and Answer the Public to build a multi-platform picture of your topic. Pull keyword volume from Google and Bing, but don’t stop there. Check TikTok and Instagram data for hashtag and creator trends. Check YouTube for video search volume. Check Amazon if your category has a commerce angle. You’re mapping where your audience is actively searching, not just where you’ve historically published.
Step two: audit your AI search presence.
For the topics where you’ve found strong demand, run them through AI Search Visibility. Which prompts is your brand appearing for? Which ones are competitors owning? The Top Prompts table will show you both. If your competitors are consistently cited for a topic your brand should own, that’s a content and PR gap. If nobody in your space is showing up consistently, that’s a first-mover opportunity.
Step three: close the gaps.
The highest-value content opportunities sit where demand is real and brand visibility is low. Those are the topics to build content around, earn citations for, and develop PR relationships that put your brand in front of journalists and creators who influence what AI models learn over time. Publishing more isn’t the goal. Publishing the right content, on the right platforms, on the topics where you’re currently invisible, is.
This framework is repeatable. Run it quarterly as your AI search visibility data evolves and as platform demand shifts. The brands that build this into their routine workflow will compound their advantage over time. For a broader foundation on getting the most out of the platform, the Ubersuggest guide is the right place to start.
FAQs
How accurate is Ubersuggest?
Ubersuggest pulls from multiple sources, including Google’s keyword planner data, to provide search volume estimates. Like any keyword tool, these are estimates rather than exact figures. For most strategy decisions, they’re directionally reliable. For AI Search Visibility, reliability is stronger because the tool aggregates data across hundreds of repeated AI queries rather than relying on a single response, which smooths out the inherent variability of AI-generated answers.
How does Ubersuggest work?
Ubersuggest combines keyword research, site audit tools, competitive analysis, and AI visibility tracking in one platform. For traditional keyword data, it pulls from search engine databases to surface volume, difficulty scores, and related terms. For AI visibility, it runs repeated queries across tools like ChatGPT and Gemini, aggregates the results, and shows how often your brand appears in those AI-generated responses compared to competitors.
How do I use Ubersuggest for keyword research?
Head to app.neilpatel.com, enter a keyword, and review the volume, keyword difficulty score, and related term suggestions. From there, you can filter by country for global demand data, use the Content Ideas tab to see which topics are already performing well in your space, or switch over to Answer the Public to pull platform-specific data from TikTok, Instagram, YouTube, and Amazon alongside traditional search engines.
Conclusion
To do marketing well in today’s world, you need to optimize for multiple platforms and regions.
SEO is no longer just a “Google” game. You must optimize for YouTube, Instagram, TikTok, ChatGPT, and all the other platforms your users use.
On top of that, you should look to expand globally.
Now, it’s too hard to tackle every country, but go after the low-hanging fruit first. What other countries have demand for your products and services? Those are the countries worth considering to move into next.
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Your longevity brand has peer-reviewed research, clinically backed products, and deep educational content covering NAD+ protocols, biomarker-driven interventions, and cellular […]
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Google is bringing Gemini into Google Ads dashboards, aiming to make data analysis more interactive, visual and accessible.
What’s happening. Google Ads is rolling out a new Dashboards feature that lets advertisers explore performance data using charts, graphs and tables, powered by Gemini.
Users can customise views simply by typing prompts, with the dashboard updating in real time based on their queries.
Why we care. Data analysis in Google Ads has traditionally required manual setup and navigation across reports.
This update shifts that workflow toward a more conversational model, where advertisers ask questions and get instant visual answers.
Zoom in. Dashboards will display key metrics like impressions, clicks, video views and cost, alongside visual breakdowns of performance across devices, audiences and campaign types.
The goal is to give advertisers a clearer, faster way to understand what’s happening in their accounts.
What to watch. How widely advertisers adopt prompt-based reporting, and whether this reduces reliance on custom-built reports and external analytics tools.
Google Discover has publisher profile pages. They live at profile.google.com/cp/ and appear when someone taps a publisher’s name on a Discover card. These pages aren’t new. They launched in August 2025 with the Follow button rollout, and by November 2025 Google’s documentation referred to them as “source overviews.”
For most of the 47,000+ publishers we monitored, the pages are auto-generated: a name, follower count, social links pulled from the Knowledge Graph, recent posts, and a footer label that reads “Profile generated by Google.”
Since March 2026, though, something changed for a small subset of publishers. A group gained access to enhanced profiles: custom banner images, a configurable links shelf, and the ability to pin posts (labeled “Pinned” in the publisher interface, formerly “Featured Posts”).
They also gained control over the order of their social links, website, and content tabs — something standard profiles don’t allow. On standard profiles, social links are sorted algorithmically by follower count, with the website listed last. On claimed profiles, the publisher decides.
The “Profile generated by Google” label also disappeared entirely, replaced by nothing — a quiet signal that the profile had been claimed.
There’s no public documentation explaining how to get access. No Search Console toggle. No application form. Google appears to have hand-selected participants for what is effectively an invitation-only pilot program.
We identified 54 publishers in this cohort. All are U.S.-based. All publish in English. And what they have — and haven’t — done with the feature over two months of monitoring reveals patterns every publisher should watch before the program scales.
How we found the 54
Our Profile Features Monitor tracks 46,926 publishers across seven languages: English, French, German, Italian, Spanish, Dutch, and Portuguese. To isolate the enhanced cohort, we filtered for publishers that showed persistent enhanced-profile signals across multiple snapshots: active links, full banner headers, or both.
The result: 54 domains with stable access to the enhanced profile surface. The composition of that group offers clues about Google’s intentions:
Tier
Publishers
Examples
National
15
WSJ, Fox News, NY Post, Newsweek, Inquirer
Regional Paper
13
Boston Globe, SFGate, CT Insider, Times Union
Local TV
14
KTLA, PIX11, MyFox8, WSMV, Atlanta News First
Lifestyle Brand
6
Delish, The Dodo, Country Living, House Beautiful
Specialty
6
Pew Research, The Athletic, Gothamist, Civil Beat
The skew toward local news and community publishers is striking and aligns with Google’s public emphasis on supporting local journalism. Nearly half the cohort — 27 of 54 publishers — consists of regional newspapers and local TV stations. National brands are included too, but they’re not the majority.
The two-tier profile system
Under the hood, Google operates two distinct profile architectures. Understanding the difference matters because this isn’t just a cosmetic upgrade. It’s a structural split.
Standard profile (99.9% of publishers):
Auto-generated from public sources.
“Profile generated by Google” label visible.
No publisher control over content or layout.
Claimed profile (the 54 publishers):
No generation label.
Publisher can configure the banner, links shelf, and pinned post.
Publisher controls the order of social links, website, and content tabs (standard profiles sort them by follower count).
This isn’t Search Console verification, structured data markup, or any existing publisher tool. It’s a separate, invitation-only system.
What the 54 publishers actually did
This is where it gets interesting. Access to a feature and its effective use are different. Here’s what the data shows across each configurable surface.
Forty-one of the 54 publishers uploaded a banner image. The remaining 13 have the capability — a “prepared” state — but haven’t used it yet.
What stands out is the production quality. There are no amateur banners in the cohort. Every uploaded image reflects clear professional design investment.
Five distinct visual archetypes emerged:
Brand-pattern: No photography, just the wordmark or abstract identity repeated as a tile. Pure prestige.
Tier predicts archetype. National publishers cluster around brand-pattern banners. Local outlets lean into civic identity and city imagery. Lifestyle brands showcase their content directly.
One anomaly: The Athletic uploaded a solid black square — 656×656 pixels. Whether that reflects deliberate minimalism aligned with The Athletic’s dark UI or simply a broken upload is unclear. It’s the only non-image banner in the cohort.
The format split is revealing: 71% used square banners — likely Google’s recommended ratio — while 29% used wide landscape formats. None used portrait layouts. Based on CDN serving patterns, the minimum recommended resolution appears to be 512 pixels on the longest side.
Publishers that chose wide formats made deliberate design decisions: SecretNYC uses a manifesto-style collage, the New York Post uses a headline grid, and Barron’s uses a geometric pattern. Square appears to be the default safe option.
Links: local TV dominates, nationals ignore it
Thirty-three of the 54 publishers enabled the links feature. Of those, 31 added at least one link, for a total of 65 configured links across the cohort.
The content is overwhelmingly focused on on-site navigation: 85% of links point to the publisher’s own sections, weather pages, live streams, or app downloads. This functions more like a mini site navigation layer than a promotional surface.
The tier gap is enormous:
Local TV: 31 links across 14 hosts (average 2.2 per publisher). Fox affiliates consistently shelve: Watch Live, Weather, Local News, Sub-region, Contact.
National: 9 links across 15 hosts (average 0.6 per publisher). Most nationals didn’t bother.
Three outliers worth noting:
PIX11 published “How to make PIX11 a preferred source on Google,” meta-promoting Discover follows from within the Discover profile itself.
Gothamist funneled donations through `pledge.wnyc.org` with a purpose-specific utm_campaign=discover-profile tag.
Fox Nation placed a direct subscription conversion link (“Subscribe to Fox Nation”) on what most publishers treat as a navigational surface.
Pinned posts (formerly Featured Posts): capability granted, rarely used
Fifty-two of the 54 publishers enabled the Pinned feature. Only 13 currently use it with an active pinned post.
Lifestyle brands were the strongest adopters: five of six had the feature active. Among national publishers, only 2 of 15 used it. The capability exists across nearly the entire cohort. Adoption does not.
About text: Wikipedia out, self-branding in
On standard profiles, the “About” section is auto-generated by Google, usually sourced from Wikipedia. On claimed profiles, publishers write their own.
Within the cohort, 38 of 54 use a custom-written description, while only 16 retain a Wikipedia-sourced version — a surprisingly low number for publishers of this size and prominence.
The tone splits cleanly by publisher tier.
Local TV stations lean promotional (“Your trusted source for breaking news, accurate weather forecasts and local sports across Greensboro…” ).
National and digital-native publishers stay more factual (“Gothamist is a website about New York City news, arts, events and food, brought to you by New York Public Radio”).
One publisher takes a mission-driven approach: Delish — “you don’t have to know how to cook, you just have to love to eat!”
The implication for publishers preparing for this feature: once you claim the profile, you take control of the About section. It becomes your pitch on a Google-owned page.
Notably, the most visible publishers in the cohort chose factual descriptions over promotional copy.
UTM tracking: the blind spot
Only three of the 65 configured links include analytics parameters. Gothamist tagged its donation link with utm_campaign=discover-profile, making it the only publisher in the cohort treating the profile as a measurable acquisition channel.
The Philadelphia Inquirer instrumented two links, but one reused an Instagram bio campaign tag (mktg_acq_ig_organic_bio_offer), meaning Discover traffic from that link will be misattributed to Instagram in analytics.
The other 62 links have no tracking at all. In practice, 95% of the cohort has no way to measure whether profile links generate traffic.
Social platform priorities
On claimed profiles, publishers control the display order of social links and content tabs. Standard profiles don’t: Google sorts links algorithmically by follower count and places the website last. That means the ordering we observe on claimed profiles reflects deliberate editorial choices, not algorithmic defaults:
Local TV stations list Facebook first: 86% (12 of 14). Zero list X/Twitter first.
National publishers spread their bets: Facebook 33%, Instagram 20%, X 20%, YouTube 13%.
Concrete examples: Newsweek places YouTube first and Articles second. Delish leads with Website, followed by Instagram. These are active editorial decisions about which audience channel matters most.
The local TV finding is particularly notable. Despite news media’s historical reliance on X/Twitter, not a single local station in this cohort places it as their primary social link.
Sister-site coordination
For media groups with multiple properties in the cohort, setup patterns reveal whether profile management is centralized or handled locally:
Hearst Connecticut, which has five papers in the cohort, shows near-identical configuration across all profiles. The links structure is the same, including a shared Hearst checkout funnel with publication-specific site IDs. The setup points to a centralized digital team managing profile operations across the group. Even so, each masthead still uses distinct banner art.
Dow Jones, across The Wall Street Journal and jp.wsj.com, uses shared banner artwork: the same wordmark tile, confirmed through perceptual hashing. That points to brand coordination at the asset level.
Everyone else Everyone else — including Fox affiliates, Dotdash Meredith properties, and the Fox News group — shows completely different setups across properties, even within owned-and-operated chains. Profile management appears to be handled locally rather than centrally.
The rollout is still active
Comparing snapshots #9 and #12 — taken 19 days apart — confirms this isn’t a frozen experiment. During that window, four publishers added banners (jp.wsj.com, New York Post, SecretNYC, and Everyday Health), one activated Links for the first time (New York Post), and jp.wsj.com (The Wall Street Journal’s Japanese edition) entered the cohort entirely.
No publishers lost features. The program is still expanding within the cohort, and new participants continue to appear.
The adoption paradox
We scored each publisher on a composite 0–6 scale, assigning one point for each of the following:
Banner uploaded
Links feature active
Featured Posts active
At least one configured link
Four or more social platforms listed
Any UTM tracking present
Nobody scored 6. The distribution:
Score
Publishers
%
2
22
41%
3
10
19%
4
14
26%
5
8
15%
6
0
0%
National publishers with the largest audiences are the least engaged with the configurable surface, with a mean score of 2.93. Most uploaded a banner and stopped there.
Local TV stations — despite having the smallest Discover footprints — are the most engaged, with a mean score of 3.57. Lifestyle brands score highest overall at 3.83, yet their Discover visibility trajectory is the flattest in the cohort.
And here’s the critical finding: feature adoption shows no correlation with visibility trajectory.
Across the cohort, the 180-day late/early capture ratio ranges from 0.23x for Prevention — down 77% — to 4.27x for NewsNation — up 327%. Variance is massive within every tier.
KTLA scores high on adoption, with seven links, a full banner, and active profile engagement, and grew 3.69x. But Delish also scores high and declined to 0.90x. MyFox8 configured five links and fell to 0.52x.
Publishers that fully utilized the configurable surface show no better visibility trajectory than those who used it minimally.
This feature gives publishers a controlled surface for branding and navigation, not a ranking lever. It’s a profile page, not an algorithm input.
What this means for publishers
The program is U.S.-only and invitation-only for now. Across the six other language markets we monitor — French, German, Italian, Spanish, Dutch, and Portuguese — we found zero enhanced profile deployments: not a single banner or configured link outside the English-language cohort.
But the underlying infrastructure is already in place. All 47,000+ publishers we track already have profile pages with follower counts, social links, and content feeds. The enhanced features sit on top of that existing architecture. Google isn’t rebuilding the system. It’s selectively unlocking capabilities within it.
If — or when — Google scales this, here’s how publishers should prepare:
Audit your structured data now. Profile social links are pulled from your sameAs/JSON-LD markup. Errors there will carry over to your profile. Verify what Google will display before you’re given control.
Design a banner. Use a square format (1:1 ratio) with a minimum resolution of 512px, and treat it as a professional brand asset. The 54 publishers in this cohort set a clear standard: there were no amateur images. Think about which archetype fits your brand: a wordmark tile for prestige brands, local landmarks for regional publishers, or content-driven imagery for vertical and lifestyle outlets.
Plan your link strategy. The data suggests that section navigation and utility content — weather, live streams, and similar recurring destinations — drive the most engagement. Local TV stations treating the profile as a mini site navigation layer are the clearest power users. Decide now which five to seven links represent your most valuable entry points.
Instrument from day one. Almost nobody in the current cohort tracks profile link performance. Adding a dedicated UTM campaign parameter — utm_campaign=discover-profile, for example — would put you ahead of 95% of the pilot group on attribution alone.
If you’re a media group, decide your operating model. Should profile management be centralized or handled newsroom by newsroom? The cohort shows both models. Hearst Connecticut runs one coordinated setup across five papers, while Fox affiliates manage profiles independently at the station level. The important part is that the choice is deliberate — not something decided accidentally when individual newsrooms start receiving invitations.
Methodology
Data comes from the 1492.vision Profile Features Monitor, which tracks roughly 47,000 publishers across seven languages through recurring snapshots of profile metadata. The 54-publisher cohort was identified through persistent enhanced-feature signals observed across multiple snapshots between March and May 2026.
Visibility trajectories are based on proprietary capture data. All findings are descriptive only: the cohort reflects Google’s selection criteria, not a random sample, and this dataset does not support causal claims about feature impact.
The full analysis — including the complete 10-phase timeline, banner image gallery, snapshot-by-snapshot evolution, and tier-by-tier breakdowns — is available at 1492.vision/research/discover-publisher-profiles-en.
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